International Business Machines Corporation

Nec Laboratories America, Inc.

Intellectual Ventures Holding 81 LLC

Merlyn Mind, Inc.

Globalfoundries Inc.

Globalfoundries U.S. 2 LLC

Sesame Workshop

Xio Research, Inc.

Ravindranath Kokku

Shivkumar Kalyanaraman

Sampath Rangarajan

Rajesh Mahindra

Prasenjit Dey

Parul Gupta

Satyanarayana Venkata Nitta

Honghai Zhang

Umamaheswari C Devi

Vinay Kumar Kolar

Dinesh C Verma

Malolan Chetlur

Vijay Ekambaram

Kang-Won Lee

Ruhi Sharma Mittal

Ramya Raghavendra

Sharad C Sundararajan

Aditya Vempaty

Mudhakar Srivatsa

Ashish Jagmohan

Tamer E Abuelsaad

Yi-Min Chee

Rong Liu

Palanivel Andiappan Kodeswaran

Patrick D Watson

Sayandeep Sen

Jae-Wook Ahn

Maria Chang

Mukundan Madhavan

Satya Nitta

Michael D Dahlin

Aditya Sinha

Praveen Yalagandula

Arunkumar Venkataramani

Shom Ponoth

Nitendra Rajput

James P Gray

Priyanka Agrawal

Kishore Ramachandran

Prithu Banerjee

Tamer Abuelsaad

Robert C Smith

Jodi M Cutler

Emily Reardon

Miles Ludwig

Nick Bartzokas

Michelle Newman-Kaplan

Robert Hutter

Satya Rama Kumar Pasumarthi

Venkatadheeraj Pichapati

Allison C Allain

Mohammad Ali Khojastepour

Ramakrishnan Rajamony

Freeman Leigh Rawson, Iii

Karthikeyan Sundaresan

Padmanabha Venkatagiri Seshadri

Yedendra Shrinivasan

Chalapathy Venkata Neti

Bikram Sengupta

Partha Dutta

Pralhad Dinesh Deshpande

Kent C Steer

Rachel Katherine Emma Bellamy

Paul V Haley

Rauf Izmailov

Hemant Jagadish Kowshik

Samrat Ganguly

Vinay Kumar Reddy

Kunal Korgaonkar

Sriram Lakshmanan

Aniruddha Bohra

Satyanarayana Nitta

Rajkumar Chandrasekaran

Flavio D Calmon

Latha Ramanan

Juan Ernesto Penarrieta Cornejo

Helene Alonso

A data management server computer ('server') is disclosed. The server receives action data associated with one or more users identifying a plurality of actions performed through one or more computer devices in a multi-modal networked environment, identifies useful sequences of actions as compound actions, and generates deep links representing the compound actions as UI elements which when invoked cause execution of the represented compound actions.

Generating compound action links in a multi-modal networked environment

Interpretation of user commands is accelerated through digital user interfaces of various modalities, including generation and presentation of command modifications for rapid correction of incomplete or erroneous user commands. An embodiment detects whether the interpreted command is accurate and, if inaccurate, precisely what was the intended command or, at least, what suggested modification to the interpreted command would be sufficient to match the intent of the user. Disambiguation occurs that entails multiple recommendation generators proposing modified commands that may more accurately reflect the intent of the user. The user may provide a response that is either a confirmation of which one of several modified commands that were automatically proposed does the user intend or a correction that computer device may use to filter or replace currently offered modified commands to generate improved modified commands. By iterative refinement, the user and computer device may quickly agree on an accurate command that the computer device should execute.

Multimodal intent entity resolver

An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Image object recognition through multimodal conversation templates

Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Image object disambiguation resolution using learner model based conversation templates

A computer-implemented method for a socially connectable agent. A non-limiting example of the computer-implemented method includes receiving, by a processor, input events. The method includes evaluating and enacting rules, by the processor, based on the received input events, where the rule include stochastic elements and temporal operators. The method pushes, by the processor, action events that result from the evaluation and enactment of the rules, and initiates, by the processor, action events.

Rules engine for social learning

A device management server computer ('server') and processing methods are disclosed. In some embodiments, the server is programmed to manage a plurality of input devices and output devices in a physical room. The server is programmed to analyze media data capturing actions performed by a user in real time as a participant in the physical room, determine how the user would like to connect at least one of the input devices and one of the output devices from the analysis, and enable the connection. The sever is programmed to interpret the actions and derive commands for connecting two or more devices based on predetermined data regarding the input devices and output devices and rules for referring to and connecting these devices.

Managing connections of input and output devices in a physical room

A computer system, method, and computer-readable product for providing data for career advice, such as job or education recommendations, from automated review and analysis of career-related data for an individual, which is at least initially obtained from documents, such as resumes and writing samples. For a designated individual, career-related data is obtained from the documents and an initial personality estimate is created for the designated individual based upon, at least, the obtained career-related data. One or more latent factor models for known career-personality matches are then gathered from a database and a questionnaire is provided to the individual to gather further information and augment the personality estimate. The created personality estimate is integrated with the latent factor model(s) to create career advice data.

Recommending personalized job recommendations from automated review of writing samples and resumes

Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Personalized video interjections based on learner model and learning objective

A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Hierarchical video concept tagging and indexing system for learning content orchestration

Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

A system for performing implicit assessment of a pupil is provided. The system receives a learning content from a content provider, the learning content having checkpoints at different stages of the learning content. The system associates assessment instructions with each checkpoint of the learning content. The system renders the received learning content for presentation to a pupil and captures the behaviors of the pupil during the presentation of the learning content. The system performs an assessment of the pupil at each checkpoint encountered during the rendering of the learning presentation. The assessment at a checkpoint includes using the checkpoint's associated assessment instructions to assign a score based on the captured behaviors. The system then produces an overall evaluation based on the assessments performed at the encountered checkpoints.

Computationally derived assessment in childhood education systems

An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.

A smart block control method, system, and computer program product, include capturing an intent of using one or more smart blocks and a domain of the smart blocks, determining an order of each of the smart blocks relative to each other, calculating an accuracy of a determined order of the smart blocks compared with the intent, and outputting an instruction via the one or more the smart blocks, in response to said calculating the accuracy of the determined order of the smart blocks compared with the predetermined order of the smart blocks.

Learning with smart blocks

A device management server computer ('server') is programmed to manage a plurality of input devices and output devices in a physical room. The server is programmed to analyze media data capturing actions performed by a user in real time as a participant in the physical room, determine how the user would like to connect at least one of the input devices and one of the output devices from the analysis, and enable the connection. The sever is programmed to interpret the actions and derive commands for connecting two or more devices based on predetermined data regarding the input devices and output devices and rules for referring to and connecting these devices.

An embodiment includes a method for use in presenting a plurality of concepts to a plurality of students. The method includes developing a first statistical model of one or more modalities effective for teaching at least a given concept of the plurality of concepts across the plurality of students. The method also includes developing a second statistical model of one or more modalities effective for learning by at least a given student of the plurality of students across the plurality of concepts. The method further includes combining the first and second models to determine one or more modalities for presenting at least the given concept to at least the given student; and presenting at least the given concept to at least the given student using the determined one or more modalities.

Personalized tutoring with automatic matching of content-modality and learner-preferences

Growing community of inventors

Hyderabad Telangana, India

Ravindranath Kokku